Land Cover Image Classification
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866914645099413504 |
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| author | Rangel, Antonio Terven, Juan Cordova-Esparza, Diana M. Chavez-Urbiola, E. A. |
| author_facet | Rangel, Antonio Terven, Juan Cordova-Esparza, Diana M. Chavez-Urbiola, E. A. |
| contents | Land Cover (LC) image classification has become increasingly significant in understanding environmental changes, urban planning, and disaster management. However, traditional LC methods are often labor-intensive and prone to human error. This paper explores state-of-the-art deep learning models for enhanced accuracy and efficiency in LC analysis. We compare convolutional neural networks (CNN) against transformer-based methods, showcasing their applications and advantages in LC studies. We used EuroSAT, a patch-based LC classification data set based on Sentinel-2 satellite images and achieved state-of-the-art results using current transformer models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_09607 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Land Cover Image Classification Rangel, Antonio Terven, Juan Cordova-Esparza, Diana M. Chavez-Urbiola, E. A. Computer Vision and Pattern Recognition Machine Learning Image and Video Processing I.2.10 Land Cover (LC) image classification has become increasingly significant in understanding environmental changes, urban planning, and disaster management. However, traditional LC methods are often labor-intensive and prone to human error. This paper explores state-of-the-art deep learning models for enhanced accuracy and efficiency in LC analysis. We compare convolutional neural networks (CNN) against transformer-based methods, showcasing their applications and advantages in LC studies. We used EuroSAT, a patch-based LC classification data set based on Sentinel-2 satellite images and achieved state-of-the-art results using current transformer models. |
| title | Land Cover Image Classification |
| topic | Computer Vision and Pattern Recognition Machine Learning Image and Video Processing I.2.10 |
| url | https://arxiv.org/abs/2401.09607 |